A Language-independent Network to Analyze the Impact of COVID-19 on the World via Sentiment Analysis

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread worldwide, resulting in a deadly pandemic that infected millions of people around the globe. The public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the lives of the people. In this paper, we study the sentiments of people from the top five worst affected countries by the virus, namely the USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net) , which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanisms to extract the positive, negative, and neutral sentiments. The network captures the subtle cues in a document by focusing on the local characteristics of text along with the past and future context information for the sentiment classification. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter and applying topic modeling to extract the hidden thematic structure of the document. The classification results demonstrate that the proposed model achieves an accuracy of 85%, which is higher than other well-known algorithms for sentiment classification. The findings show that the topics which evoked positive sentiments were related to frontline workers, entertainment, motivation, and spending quality time with family. The negative sentiments were related to socio-economic factors like racial injustice, unemployment rates, fake news, and deaths. Finally, this study provides feedback to the government and health professionals to handle future outbreaks and highlight future research directions for scientists and researchers.

Molecular Epidemiology of Plasmodium species in Conflicted Federally Administered Tribal Area (FATA) Pakistan

Military conflicts have been significant obstacles in detecting and treating infectious disease diseases due to the diminished public health infrastructure, resulting in malaria endemicity. A variety of violent and destructive incidents were experienced by FATA (Federally Administered Tribal Areas). It was a struggle to pursue an epidemiological analysis due to continuing conflict and Talibanization. Clinical isolates were collected from Bajaur, Mohmand, Khyber, Orakzai agencies from May 2017 to May 2018. For Giemsa staining, full blood EDTA blood samples have been collected from symptomatic participants. Malaria-positive microscopy isolates were spotted on filter papers for future Plasmodial molecular detection by nested polymerase chain reaction (nPCR) of small subunit ribosomal ribonucleic acid (ssrRNA) genes specific primers. Since reconfirming the nPCR, a malariometric study of 762 patients found 679 positive malaria cases. Plasmodium vivax was 523 (77%), Plasmodium falciparum 121 (18%), 35 (5%) were with mixed-species infection (P. vivax plus P. falciparum), and 83 were declared negative by PCR. Among the five agencies of FATA, Khyber agency has the highest malaria incidence (19%) with followed by P. vivax (19%) and P. falciparum (4.1%). In contrast, Kurram has about (14%), including (10.8%) P. vivax and (2.7%) P. falciparum cases, the lowest malaria epidemiology. Surprisingly, no significant differences in the distribution of mixed-species infection among all five agencies. P. falciparum and P. vivax were two prevalent FATA malaria species in Pakistan’s war-torn area. To overcome this rising incidence of malaria, this study recommends that initiating malaria awareness campaigns in school should be supported by public health agencies and malaria-related education locally, targeting children and parents alike.

“It’s good to feel like you’re doing something”: a qualitative study examining state health department employees’ views on why ineffective programs continue to be implemented in the USA

Background Mis-implementation, the inappropriate continuation of programs or policies that are not evidence-based or the inappropriate termination of evidence-based programs and policies, can lead to the inefficient use of scarce resources in public health agencies and decrease the ability of these agencies to deliver effective programs and improve population health. Little is known about why mis-implementation occurs, which is needed to understand how to address it. This study sought to understand the state health department practitioners’ perspectives about what makes programs ineffective and the reasons why ineffective programs continue. Methods Eight state health departments (SHDs) were selected to participate in telephone-administered qualitative interviews about decision-making around ending or continuing programs. States were selected based on geographic representation and on their level of mis-implementation (low and high) categorized from our previous national survey. Forty-four SHD chronic disease staff participated in interviews, which were audio-recorded and transcribed verbatim. Transcripts were consensus coded, and themes were identified and summarized. This paper presents two sets of themes, related to (1) what makes a program ineffective and (2) why ineffective programs continue to be implemented according to SHD staff. Results Participants considered programs ineffective if they were not evidence-based or if they did not fit well within the population; could not be implemented well due to program restraints or a lack of staff time and resources; did not reach those who could most benefit from the program; or did not show the expected program outcomes through evaluation. Practitioners described several reasons why ineffective programs continued to be implemented, including concerns about damaging the relationships with partner organizations, the presence of program champions, agency capacity, and funding restrictions. Conclusions The continued implementation of ineffective programs occurs due to a number of interrelated organizational, relational, human resources, and economic factors. Efforts should focus on preventing mis-implementation since it limits public health agencies’ ability to conduct evidence-based public health, implement evidence-based programs effectively, and reduce the high burden of chronic diseases. The use of evidence-based decision-making in public health agencies and supporting adaptation of programs to improve their fit may prevent mis-implementation. Future work should identify effective strategies to reduce mis-implementation, which can optimize public health practice and improve population health.

Vaccine Acceptance and Hesitancy among College Students in Nevada: A State-Wide Cross-Sectional Study

The purpose of this study was to identify factors related to COVID-19 vaccine acceptance and hesitancy in a diverse state-wide population of students. An electronic survey was emailed to students in the Nevada System of Higher Education to assess effects of the pandemic. The survey included questions related to vaccine status, interest in receiving the COVID-19 vaccine, factors influencing these decisions, and sources of health information. Among the 3773 respondents, over half (54%) were accepting of the vaccine, including vaccinated students (18.9%). Nearly one quarter (23.5%) expressed hesitancy to receive the vaccine, citing concerns about side effects and the need for more research. Factors related to hesitancy included female gender, increasing age, place of residence, marital status, and Black or Native American race. Vaccine hesitant respondents were less likely than other respondents to rely on public health agencies or newspapers for health information, and more likely to rely on employers, clinics, or “no one”. Culturally appropriate efforts involving COVID-19 vaccine information and distribution should target certain groups, focusing on factors such as side effects, development and testing of the vaccine. Research should investigate sources of health information of people who are hesitant to receive vaccines.

Application of Big Data in Digital Epidemiology

Epidemiology is the study of dynamics of health and disease in human population. It aims to identify the occurrence, pattern, and etiology of human diseases so that the causes of these diseases can be understood, which in turn will help in preventing their spread. In traditional epidemiology, the data is collected by various public health agencies through various means. Many times, the actual figures vary a lot from the one reported. Sometimes this difference is due to human errors, but most of the time, it is due to intentional underreporting. Big data techniques can be used to analyze this huge amount of data so as to extract useful information from it. The electronic health data is so large and complex that it cannot be processed using traditional software and hardware. It is also not possible to manage this data using traditional data management tools. This data is huge in terms of volume as well as diversity and the speed at which it is being generated. The ability to combine and analyze these different sources of data has huge impact on epidemic tracking.

Identification of public submitted tick images: A neural network approach

Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated with encountered ticks and by providing researchers and public health agencies with additional data on tick activity and geographic range. Here we outline the requirements for such a system, present a model that meets those requirements, and discuss remaining challenges and frontiers in automated tick identification. We compiled a user-generated dataset of more than 12,000 images of the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. We used image augmentation to further increase the size of our dataset to more than 90,000 images. Here we report the development and validation of a convolutional neural network which we call “TickIDNet,” that scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network’s ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet’s performance can be increased by using confidence thresholds to introduce an “unsure” class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.

Considerations for Improving Reporting and Analysis of Date-Based COVID-19 Surveillance Data by Public Health Agencies

More than a year after the first domestic COVID-19 cases, the United States does not have national standards for COVID-19 surveillance data analysis and public reporting. This has led to dramatic variations in surveillance practices among public health agencies, which analyze and present newly confirmed cases by a wide variety of dates. The choice of which date to use should be guided by a balance between interpretability and epidemiological relevance. Report date is easily interpretable, generally representative of outbreak trends, and available in surveillance data sets. These features make it a preferred date for public reporting and visualization of surveillance data, although it is not appropriate for epidemiological analyses of outbreak dynamics. Symptom onset date is better suited for such analyses because of its clinical and epidemiological relevance. However, using symptom onset for public reporting of new confirmed cases can cause confusion because reporting lags result in an artificial decline in recent cases. We hope this discussion is a starting point toward a more standardized approach to date-based surveillance. Such standardization could improve public comprehension, policymaking, and outbreak response. (Am J Public Health. 2021;111(12):2127–2132. https://doi.org/10.2105/AJPH.2021.306520 )

Vaccines for the COVID-19 α Variant: An Econometric Analysis

This study examines the success of COVID-19 vaccines in four European countries and Israel for the α variant. These countries respond to the vaccines with varying degrees of success. Countries with successful vaccination programs take about 160 days to get to the minimum number of new cases. Only Italy and Israel came close to eradicating the virus. Vaccines and previous infections have a similar prophylactic effect on new infections. Second doses for the most part add little protection to those who have only one dose. Vaccines become very effective after seven days although there are some added benefits that accrue to individuals in the second week after vaccination. The effect of vaccines on new cases is non-linear and exhibits a decreasing marginal effect. COVID-19 is spread by asymptomatic carriers, a feature of the disease which was discernable at the same time that public health agencies were discouraging the use of masks by the general public and downplaying the importance of social distancing. These were major policy errors and led to many unnecessary deaths.

Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences

The open sharing of genomic data provides an incredibly rich resource for the study of bacterial evolution and function and even anthropogenic activities such as the widespread use of antimicrobials. However, these data consist of genomes assembled with different tools and levels of quality checking, and of large volumes of completely unprocessed raw sequence data. In both cases, considerable computational effort is required before biological questions can be addressed. Here, we assembled and characterised 661,405 bacterial genomes retrieved from the European Nucleotide Archive (ENA) in November of 2018 using a uniform standardised approach. Of these, 311,006 did not previously have an assembly. We produced a searchable COmpact Bit-sliced Signature (COBS) index, facilitating the easy interrogation of the entire dataset for a specific sequence (e.g., gene, mutation, or plasmid). Additional MinHash and pp-sketch indices support genome-wide comparisons and estimations of genomic distance. Combined, this resource will allow data to be easily subset and searched, phylogenetic relationships between genomes to be quickly elucidated, and hypotheses rapidly generated and tested. We believe that this combination of uniform processing and variety of search/filter functionalities will make this a resource of very wide utility. In terms of diversity within the data, a breakdown of the 639,981 high-quality genomes emphasised the uneven species composition of the ENA/public databases, with just 20 of the total 2,336 species making up 90% of the genomes. The overrepresented species tend to be acute/common human pathogens, aligning with research priorities at different levels from individual interests to funding bodies and national and global public health agencies.